Multilingual Age of Exposure 2.0
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| Title: | Multilingual Age of Exposure 2.0 |
|---|---|
| Language: | English |
| Authors: | Robert-Mihai Botarleanu (ORCID |
| Source: | International Journal of Artificial Intelligence in Education. 2024 34(4):1353-1377. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 25 |
| Publication Date: | 2024 |
| Sponsoring Agency: | Institute of Education Sciences (ED) Office of Naval Research (ONR) (DOD) |
| Contract Number: | R305A180261 R305A190050 N000142012623 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Multilingualism, English (Second Language), Second Language Learning, Second Language Instruction, Age Differences, Semantics, Word Lists, Vocabulary Development, Artificial Intelligence, Scores, Simulation, Networks, Prediction, Models, Computational Linguistics, Natural Language Processing, Spanish, French, German |
| DOI: | 10.1007/s40593-023-00386-7 |
| ISSN: | 1560-4292 1560-4306 |
| Abstract: | Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word's semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction models for other languages. Our main contributions are threefold. First, we introduce a novel AoE regression architecture that uses a Recurrent Neural Network applied to the simulated word exposure trajectories. Second, we consider word embeddings projected into a unified multilingual space. Third, we apply transfer learning on the English AoE regressor to improve the performance of non-English AoE regressors. We show that AoA lists across languages share inherent similarities that enable Machine Learning models to transfer insights from one language to another, thus diminishing the effect of the smaller sample sizes for non-English languages. |
| Abstractor: | As Provided |
| Notes: | https://github.com/readerbench/Age-of-Exposure/tree/master/resources |
| IES Funded: | Yes |
| Entry Date: | 2024 |
| Accession Number: | EJ1453674 |
| Database: | ERIC |
| Abstract: | Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word's semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction models for other languages. Our main contributions are threefold. First, we introduce a novel AoE regression architecture that uses a Recurrent Neural Network applied to the simulated word exposure trajectories. Second, we consider word embeddings projected into a unified multilingual space. Third, we apply transfer learning on the English AoE regressor to improve the performance of non-English AoE regressors. We show that AoA lists across languages share inherent similarities that enable Machine Learning models to transfer insights from one language to another, thus diminishing the effect of the smaller sample sizes for non-English languages. |
|---|---|
| ISSN: | 1560-4292 1560-4306 |
| DOI: | 10.1007/s40593-023-00386-7 |
